Monetize Risk Intelligence: Architect and deploy production-grade AI/ML frameworks to monetize Fiserv’s unique data footprint into inventive risk scores and insights that detect identity, transaction, and business-level threats.
Architect Financial AI: Build custom GenAI, NLP, and LLM models for high-velocity stream processing, focusing on extracting risk indicators and behavioral anomalies from structured transaction data and unstructured metadata.
Next-Gen Frameworks: Implement LangChain and LlamaIndex to develop RAG and Agentic AI frameworks that enable institutional clients to query and interact with complex, multi-dimensional risk datasets.
Quantitative Collaboration: Work in a high-performance team environment, collaborating with Product Managers, payment system experts, and Engineering to deploy and monitor production-grade AI and ML models.
Strategic Synthesis: Distill complex quantitative risk insights into high-level investment and risk theses for executive leadership and sophisticated external stakeholders.
Data Stewardship & Compliance: Partner with the Data Usage Committee, Model Governance, Legal, and Compliance teams to ensure data privacy and adherence to strict data usage rights within the DCS framework.

Requirements

7+ years of experience leveraging large scale datasets to develop tactical insights into fraud typologies such as ATO, Synthetic ID, and AML using ML, RAG, and NLP.
7+ years of experience formulating research problems, designing champion/challenger back-tests, and implementing production-ready solutions in a financial or high-growth tech environment.
Experience with anomaly detection, credit risk modeling, and adversarial machine learning within merchant and banking ecosystems.
Mastery of high-precision classification, anomaly detection, and clustering techniques, focusing on non-stationary time series analysis, Bayesian inference, causal analysis, and survival analysis to model risk probabilities, event timing, and evolving fraud trends.
Expert proficiency in Python, SQL, and PySpark for high-volume transaction processing, with hands on use of Scikit-learn, XGBoost, LightGBM, and Deep Learning and Agentic AI frameworks for threat hunting, and graph databases like Neo4j or Tiger graph for fraud network analysis.
Experience with Databricks and Snowflake, SageMaker or Azure ML, feature stores (e.g., Tecton, Feast) and streaming architectures (Kafka, Flink).
Proficiency in tokenization and embeddings, with hands-on experience tuning and deploying Large Language Model architectures such as LLaMA, BERT, or Transformers.
Bachelor’s degree in a quantitative field such as Computer Science, Mathematics, Artificial Intelligence, Financial Engineering, or Statistics.

Benefits

Annual incentive opportunity, which may be delivered as a mix of cash bonus and equity awards

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI frameworksML frameworksGenAINLPLLM modelsanomaly detectioncredit risk modelingclassification techniquesBayesian inferencesurvival analysis

Soft Skills

collaborationstrategic synthesisquantitative analysis